In recent years, study has been actively undertaken wherein a user's state is modeled and learned using time series data obtained from a wearable sensor which is a sensor to be wearable by a user, and the user's current state is recognized using a model obtained by learning (see PTLs 1 and 2, and NPL 1).
The present applicant has previously proposed a method for stochastically predicting multiple probabilities of a user's activity state in predetermined point-in-time in the future as Japanese Patent Application No. 2009-180780 (hereinafter, referred to as prior application 1). With the method according to the prior application 1, a user's activity state is learned from time series data as a probabilistic state transition model, and the current activity state is recognized using the learned probabilistic state transition model, and accordingly, a user's activity state “after predetermined time” may probabilistically predict. With the prior application 1, an example is illustrated as an example of prediction of a user's activity state “after predetermined time” wherein a user's current position is recognized using a probabilistic state transition model in which the user's movement history time series data (movement history data) has been learned to predict the user's destination (place) after predetermined time.
Further, the present applicant has developed on the prior application 1 and proposed a method for predicting arrival probability, route, and time for multiple destinations even in the event that there is no specification of elapse time from the current point-in-time serving as “after predetermined time”, as Japanese Patent Application No. 2009-208064 (hereinafter, referred to as the prior application 2). With the method according to the prior application 2, an attribute of “moving state” or “stay state” is added to a state node making up a probabilistic state transition model. A destination candidate may automatically be detected by finding a state node in “stay state” as a state node for the destination, out of state nodes making up the probabilistic state transition model.
The present applicant has enabled the learning model (probabilistic state transition model) according to the prior application 2 to be developed when movement history data of a new movement route is added as Japanese Patent Application No. 2010-141946 (hereinafter, referred to as the prior application 3), thereby enabling effective learning.